经济学权威期刊Journal of Business & Economic Statistics 2023年第1期
Journal of Business & Economic Statistics 2023年第1期
——更多动态，请持续关注gzh：理想主义的百年孤独
1.Reconciling Trends in U.S. Male Earnings Volatility: Results from Survey and Administrative Data
调和美国男性收入波动的趋势:来自调查和行政数据的结果
Robert Moffitt, John Abowd, Christopher Bollinger, Michael Carr, Charles Hokayem, Kevin McKinney, Emily Wiemers, Sisi Zhang & James Ziliak
There is a large literature on earnings and income volatility in labor economics, household finance, and macroeconomics. One strand of that literature has studied whether individual earnings volatility has risen or fallen in the United States over the last several decades. There are strong disagreements in the empirical literature on this important question, with some studies showing upward trends, some showing downward trends, and some showing no trends. Some studies have suggested that the differences are the result of using flawed survey data instead of more accurate administrative data. This article summarizes the results of a project attempting to reconcile these findings with four different datasets and six different data series—three survey and three administrative data series, including two which match survey respondent data to their administrative data. Using common specifications, measures of volatility, and other treatments of the data, four of the six data series show a lack of any significant long-term trend in male earnings volatility over the last 20-to-30+ years when differences across the datasets are properly accounted for. A fifth data series (the PSID) shows a positive net trend but small in magnitude. A sixth, administrative, dataset, available only since 1998, shows no net trend 1998–2011 and only a small decline thereafter. Many of the remaining differences across data series can be explained by differences in their cross-sectional distribution of earnings, particularly differences in the size of the lower tail. We conclude that the datasets we have analyzed, which include many of the most important available, show little evidence of any significant trend in male earnings volatility since the mid-1980s.
在劳动经济学、家庭金融和宏观经济学中，有大量关于收入和收入波动的文献。其中一组文献研究了美国过去几十年个人收入波动性是上升还是下降。关于这一重要问题的实证文献存在着强烈的分歧，有的研究呈现上升趋势，有的研究呈现下降趋势，有的研究没有趋势。一些研究表明，造成这种差异的原因是使用了有缺陷的调查数据，而不是更准确的管理数据。本文总结了一个项目的结果，该项目试图用四个不同的数据集和六个不同的数据系列来协调这些发现-三个调查和三个行政数据系列，其中两个将调查对象的数据与其行政数据进行匹配。使用共同的规格、波动性的衡量和对数据的其他处理，六个数据系列中的四个显示，在适当考虑到数据集之间的差异时，过去20至30多年来，男性收入波动性缺乏任何显著的长期趋势。第五个数据系列(PSID)显示了积极的净趋势，但规模较小。第6个数据集(行政数据集)仅在1998年之后提供，显示1998 - 2011年没有净趋势，此后只有小幅下降。数据序列之间的许多剩余差异可以用它们的横截面收入分布的差异来解释，特别是下尾大小的差异。我们得出的结论是，我们分析的数据集(包括许多最重要的可用数据)几乎没有证据表明，自20世纪80年代中期以来，男性收入波动出现了任何显著趋势。
2.Trends in Earnings Volatility Using Linked Administrative and Survey Data
使用关联管理和调查数据的盈利波动趋势
James P. Ziliak, Charles Hokayem & Christopher R. Bollinger
We document trends in earnings volatility separately by gender using unique linked survey data from the CPS ASEC and Social Security earnings records for the tax years spanning 1995–2015. The exact data link permits us to focus on differences in measured volatility from earnings nonresponse, survey attrition, and measurement between survey and administrative earnings data reports, while holding constant the sampling frame. Our results for both men and women suggest that the level and trend in volatility is similar in the survey and administrative data, showing substantial business-cycle sensitivity among men but no overall trend among continuous workers, while women demonstrate no change in earnings volatility over the business cycle but a declining trend. A substantive difference emerges with the inclusion of imputed earnings among survey nonrespondents, suggesting that users of the ASEC drop earnings nonrespondents.
我们使用CPS ASEC和1995-2015纳税年度社会保障收入记录的独特关联调查数据，按性别分别记录了收入波动趋势。准确的数据链接使我们能够在保持抽样框架不变的情况下，专注于从盈余不回应、调查减员以及调查和行政盈余数据报告之间的测量波动的差异。我们对男性和女性的研究结果表明，调查和管理数据中的波动水平和趋势相似，男性的收入波动在商业周期中具有很大的敏感性，但在连续工作者中没有总体趋势，而女性的收入波动在商业周期中没有变化，而是呈下降趋势。在纳入未被调查对象的估算收入时，出现了实质性的差异，这表明ASEC的用户减少了未被调查对象的收入。
3.Estimating Trends in Male Earnings Volatility with the Panel Study of Income Dynamics
用收入动态的小组研究估计男性收入波动的趋势
Robert Moffitt & Sisi Zhang
The Panel Study of Income Dynamics (PSID) has been the workhorse dataset used to estimate trends in U.S. earnings volatility at the individual level. We provide updated estimates for male earnings volatility using additional years of data. The analysis confirms prior work showing upward trends in the 1970s and 1980s, with a near doubling of the level of volatility over that period. The results also confirm prior work showing a resumption of an upward trend starting in the 2000s, but the new years of data available show volatility to be falling in recent years. By 2018, volatility had grown by a modest amount relative to the 1990s, with a growth rate only one-fifth the magnitude of that in the 1970s and 1980s. We show that neither attrition or item nonresponse bias, nor other issues with the PSID, affect these conclusions.
收入动态面板研究(PSID)一直是用于估计美国个人层面盈利波动趋势的主力数据集。我们使用额外年份的数据提供男性收入波动的最新估计。该分析证实了之前的研究显示，20世纪70年代和80年代出现了上升趋势，在此期间的波动水平几乎翻了一番。研究结果还证实了之前的研究结果，即从2000年代开始，美国经济恢复了上升趋势，但可获得的新一年数据显示，近年来波动性正在下降。到2018年，相对于上世纪90年代，波动性略有增长，增速仅为上世纪70年代和80年代的五分之一。我们表明，无论是磨损或项目无反应偏差，或PSID的其他问题，都不会影响这些结论。
4.Reconciling Trends in Male Earnings Volatility: Evidence from the SIPP Survey and Administrative Data
调和男性收入波动的趋势:来自SIPP调查和行政数据的证据
Michael D. Carr, Robert A. Moffitt & Emily E. Wiemers
As part of a set of papers using the same methods and sample selection criteria to estimate trends in male earnings volatility across survey and administrative datasets, we conduct a new investigation of male earnings volatility using data from the Survey of Income and Program Participation (SIPP) survey and SIPP-linked administrative earnings data (SIPP GSF). We find that the level of volatility is higher in the administrative earnings histories in the SIPP GSF than in the SIPP survey but that the trends are similar. Between 1984 and 2012, volatility in the SIPP survey declines slightly while volatility in the SIPP GSF increases slightly. Including imputations due to unit nonresponse in the SIPP survey data increases both the level and upward trend in volatility and poses a challenge for estimating a consistent series in the SIPP survey data. Because the density of low earnings differs considerably across datasets, and volatility may vary across the earnings distribution, we also estimate trends in volatility where we hold the earnings distribution fixed across the two data sources. Differences in the underlying earnings distribution explain much of the difference in the level of and trends in volatility between the SIPP survey and SIPP GSF.
作为一套使用相同方法和样本选择标准来估计调查和管理数据集男性收入波动趋势的论文的一部分，我们使用收入和项目参与调查(SIPP)和SIPP相关管理收入数据(SIPP GSF)的数据对男性收入波动进行了新的调查。我们发现，与SIPP调查相比，SIPP GSF的行政盈余历史波动水平更高，但趋势相似。1984—2012年期间，SIPP调查的波动性略有下降，而SIPP GSF的波动性略有上升。在SIPP调查数据中加入单位无响应的估算会增加波动水平和上升趋势，并对SIPP调查数据中一致性序列的估计提出了挑战。由于低收益的密度在不同数据集之间存在很大差异，而波动性可能在不同的盈利分布之间存在差异，我们还在两个数据源之间保持盈利分布固定的情况下估计波动性的趋势。基础收益分配的差异在很大程度上解释了SIPP调查和SIPP GSF之间波动水平和趋势的差异。
5.Male Earnings Volatility in LEHD Before, During, and After the Great Recession
在大衰退之前、期间和之后，LEHD的男性收入波动
Kevin L. McKinney & John M. Abowd
This article is part of a coordinated collection of papers on prime-age male earnings volatility. Each paper produces a similar set of statistics for the same reference population using a different primary data source. Our primary data source is the Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) infrastructure files. Using LEHD data from 1998 to 2016, we create a well-defined population frame to facilitate accurate estimation of temporal changes comparable to designed longitudinal samples of people. We show that earnings volatility, excluding increases during recessions, has declined over the analysis period, a finding robust to various sensitivity analyses.
本文是关于壮年男性收入波动的一系列论文的一部分。每篇论文都使用不同的主要数据源对相同的参考人口产生了一组类似的统计数据。我们的主要数据来源是人口普查局的纵向雇主-家庭动态(LEHD)基础设施文件。利用1998年至2016年的LEHD数据，我们创建了一个定义良好的人口框架，以促进与设计的纵向人群样本相比较的时间变化的准确估计。我们发现，除去经济衰退期间的增长，盈利波动性在分析期间有所下降，这一发现对各种敏感性分析都是稳健的。
6.Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach
高维时间序列中条件协方差矩阵的预测:一般动态因子方法
Carlos Trucíos, João H. G. Mazzeu, Marc Hallin, Luiz K. Hotta, Pedro L. Valls Pereira & Mauricio Zevallos
Based on a General Dynamic Factor Model with infinite-dimensional factor space and MGARCH volatility models, we develop new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The finite-sample performance of our approach is evaluated via Monte Carlo experiments and outperforms the most alternative methods. This new approach is also used to construct minimum one-step-ahead variance portfolios for a high-dimensional panel of assets. The results are shown to match the results of recent proposals by Engle, Ledoit, and Wolf and achieve better out-of-sample portfolio performance than alternative procedures proposed in the literature.
基于具有无限维因子空间的一般动态因子模型和MGARCH波动率模型，我们开发了高维时间序列条件协方差矩阵的估计和预测方法。我们的方法的有限样本性能通过蒙特卡罗实验进行评估，并优于大多数替代方法。这种新方法也被用于构造一个高维资产面板的最小一步前方差投资组合。结果显示，与Engle、Ledoit和Wolf最近的建议相匹配，并取得了比文献中提出的替代程序更好的样本外投资组合业绩。
7.Volatility Estimation When the Zero-Process is Nonstationary
零过程非平稳时的波动率估计
Christian Francq & Genaro Sucarrat
Financial returns are frequently nonstationary due to the nonstationary distribution of zeros. In daily stock returns, for example, the nonstationarity can be due to an upwards trend in liquidity over time, which may lead to a downwards trend in the zero-probability. In intraday returns, the zero-probability may be periodic: It is lower in periods where the opening hours of the main financial centers overlap, and higher otherwise. A nonstationary zero-process invalidates standard estimators of volatility models, since they rely on the assumption that returns are strictly stationary. We propose a GARCH model that accommodates a nonstationary zero-process, derive a zero-adjusted QMLE for the parameters of the model, and prove its consistency and asymptotic normality under mild assumptions. The volatility specification in our model can contain higher order ARCH and GARCH terms, and past zero-indicators as covariates. Simulations verify the asymptotic properties in finite samples, and show that the standard estimator is biased. An empirical study of daily and intradaily returns illustrate our results. They show how a nonstationary zero-process induces time-varying parameters in the conditional variance representation, and that the distribution of zero returns can have a strong impact on volatility predictions.
由于零的非平稳分布，财务回报往往是非平稳的。例如，在每日股票收益中，非平稳性可能是由于随着时间的推移，流动性呈上升趋势，这可能导致零概率呈下降趋势。在日内回报率中，零概率可能是周期性的:在主要金融中心开放时间重叠的时期，零概率较低，反之则较高。非平稳零过程使波动率模型的标准估计失效，因为它们依赖于收益是严格平稳的假设。本文提出了一个适应非平稳零过程的GARCH模型，给出了模型参数的零调整QMLE，并证明了该模型在温和假设下的一致性和渐近正态性。在我们的模型中，波动率规范可以包含更高阶的ARCH和GARCH项，以及过去的零指标作为协变量。仿真验证了有限样本下的渐近性质，并证明了标准估计是有偏的。对每日和每日内部回报的实证研究说明了我们的结果。它们展示了非平稳零过程如何在条件方差表示中诱导时变参数，以及零回报的分布可能对波动率预测产生强烈影响。
8.Composite Index Construction with Expert Opinion
结合专家意见构建综合指数
Rong Chen, Yuanyuan Ji, Guolin Jiang, Han Xiao, Ruoqing Xie & Pingfang Zhu
Composite index is a powerful and popularly used tool in providing an overall measure of a subject by summarizing a group of measurements (component indices) of different aspects of the subject. It is widely used in economics, finance, policy evaluation, performance ranking, and many other fields. Effective construction of a composite index has been studied extensively. The most widely used approach is to use a linear combination of the component indices, where the combination weights are determined by optimizing an objective function. To maximize the overall variation of the resulting composite index, the combination weights can be obtained through principal component analysis. In this article, we propose to incorporate expert opinions into the construction of the composite index. It is noted that expert opinion often provides useful information in assessing which of the component indices are more important for the overall measure of the subject. We consider the case that a group of experts have been consulted, each providing a set of importance scores for the component indices, along with a set of confidence scores which reflects the expert’s own confidence in his/her assessment. In addition, the constructor of the composite index can also provide an assessment of the expertise level of each expert. We use linear combinations to construct the composite index, where the combination weights are determined by maximizing the sum of resulting composite index variation and the negative weighted sum of squares of deviation between the combination weights used and the experts’ scores. A data-driven approach is used to find the optimal balance between the two sources of information. Theoretical properties of the procedure are investigated. Simulation examples and an economic application on constructing science and technology development index is carried out to illustrate the proposed method.
综合指数是一种功能强大且被广泛使用的工具，它通过汇总一组主体不同方面的度量(组成指数)来提供对主体的总体度量。它被广泛应用于经济、金融、政策评价、绩效排名等诸多领域。综合指数的有效构建已被广泛研究。最广泛使用的方法是使用组成指标的线性组合，其中组合权重通过优化目标函数来确定。为了使得到的综合指标的整体变化最大化，可以通过主成分分析得到组合权重。在本文中，我们建议将专家意见纳入到综合指数的构建中。有人指出，专家意见往往提供有用的资料，以评估哪些组成指数对该问题的全面衡量更为重要。我们认为，已经咨询了一组专家，每个专家都为组成指数提供了一套重要分数，以及一套信心分数，这反映了专家自己对其评估的信心。此外，综合指数的构造者还可以对每个专家的专业水平进行评估。我们使用线性组合来构建综合指标，其中组合权重是通过最大化所得到的综合指标变异和所使用的组合权重与专家得分的负加权偏差平方和来确定的。数据驱动方法用于在两个信息源之间找到最佳平衡。研究了该过程的理论性质。通过仿真实例和构建科技发展指数的经济应用来说明所提出的方法。
9.Panel Stochastic Frontier Model With Endogenous Inputs and Correlated Random Components
具有内生输入和相关随机分量的面板随机前沿模型
Lai Hung-pin & Subal C. Kumbhakar
In this article, we consider a panel stochastic frontier model in which the composite error term εit has four components, that is, εit=τi−ηi+vit−uit, where ηi and uit are persistent and transient inefficiency components, τi consists of the random firm effects and vit is the random noise. Two distinguishing features of the proposed model are (i) the inputs are allowed to be correlated with one or more of the error components in the production function; (ii) time-invariant and time-varying components, that is, (τi−ηi) and (vit−uit), are allowed to be correlated. To keep the formulation general, we do not specify whether this correlation comes from the correlations between (i) ηi and uit, (ii) τi and uit, (iii) τi and vit, (iv) ηi and vit, or some other combination of them. Further, we also consider the case when the correlation in the composite error arises from the time dependence of εit. To estimate the model parameters and predict (in)efficiency, we propose a two-step procedure. In the first step, either the within or the first difference transformation that eliminates the time-invariant components is proposed. We then use either the 2SLS or the GMM approach to obtain unbiased and consistent estimators of the parameters in the frontier function, except for the intercept. In the second step, the maximum simulated likelihood method is used to estimate the parameters associated with the distributions of τi and vit, ηi and uit as well as the intercept. The copula approach is used in this step to model the dependence between the time-varying and time-invariant components. Formulas to predict transient and persistent (in)efficiency are also derived. Finally, results from both simulated and real data are provided.
在本文中，我们考虑一个面板随机前沿模型，其中复合误差项εit有四个分量，即εit=τi - ηi+vit - uit，其中ηi和uit是持续的和瞬态的无效率分量，τi由随机企业效应组成，vit是随机噪声。该模型的两个显著特征是(i)输入允许与生产函数中的一个或多个误差分量相关;(ii)允许时不变和时变分量，即(τi - ηi)和(vit - uit)相关。为了使公式一般化，我们没有具体说明这种相关性是否来自(i) ηi和uit， (ii) τi和uit， (iii) τi和vit， (iv) ηi和vit，或它们的其他组合。此外，我们还考虑了复合误差中的相关性是由εit的时间依赖性引起的情况。为了估计模型参数和预测(in)效率，我们提出了一个两步程序。第一步，提出了消除时不变分量的内差分变换和一阶差分变换。然后，我们使用2SLS或GMM方法来获得除截距外的边界函数参数的无偏和一致估计。第二步，利用最大似然法估计τi和vit、ηi和uit分布的相关参数以及截距。在这一步中使用copula方法来建模时变和时不变分量之间的依赖关系。推导了瞬态和持续(in)效率的计算公式。最后给出了模拟和实际数据的结果。
10.Optimal Covariate Balancing Conditions in Propensity Score Estimation
倾向得分估计中的最优协变量平衡条件
Jianqing Fan, Kosuke Imai, Inbeom Lee, Han Liu, Yang Ning & Xiaolin Yang
Inverse probability of treatment weighting (IPTW) is a popular method for estimating the average treatment effect (ATE). However, empirical studies show that the IPTW estimators can be sensitive to the misspecification of the propensity score model. To address this problem, researchers have proposed to estimate propensity score by directly optimizing the balance of pretreatment covariates. While these methods appear to empirically perform well, little is known about how the choice of balancing conditions affects their theoretical properties. To fill this gap, we first characterize the asymptotic bias and efficiency of the IPTW estimator based on the covariate balancing propensity score (CBPS) methodology under local model misspecification. Based on this analysis, we show how to optimally choose the covariate balancing functions and propose an optimal CBPS-based IPTW estimator. This estimator is doubly robust; it is consistent for the ATE if either the propensity score model or the outcome model is correct. In addition, the proposed estimator is locally semiparametric efficient when both models are correctly specified. To further relax the parametric assumptions, we extend our method by using a sieve estimation approach. We show that the resulting estimator is globally efficient under a set of much weaker assumptions and has a smaller asymptotic bias than the existing estimators. Finally, we evaluate the finite sample performance of the proposed estimators via simulation and empirical studies. An open-source software package is available for implementing the proposed methods.
处理加权反概率法(IPTW)是估计平均处理效应(ATE)的一种常用方法。然而，实证研究表明，IPTW估计量对倾向评分模型的错误描述很敏感。针对这一问题，研究者提出通过直接优化预处理协变量的平衡来估计倾向得分。虽然这些方法似乎在经验上表现良好，但很少知道平衡条件的选择如何影响它们的理论性质。为了填补这一空白，我们首先描述了在局部模型错误描述下，基于协变量平衡倾向得分(CBPS)方法的IPTW估计器的渐近偏差和效率。在此基础上，我们展示了如何最优地选择协变量平衡函数，并提出了一个最优的基于cbps的IPTW估计器。该估计量具有双重鲁棒性;如果倾向得分模型或结果模型都是正确的，则对ATE来说是一致的。此外，当两个模型都被正确指定时，所提出的估计是局部半参数有效的。为了进一步放宽参数假设，我们使用了一个筛估计方法来扩展我们的方法。我们证明了所得到的估计量在一组更弱的假设下是全局有效的，并且有一个比现有估计量更小的渐近偏差。最后，我们通过仿真和实证研究评估了所提出的估计器的有限样本性能。一个开源软件包可用于实现所提出的方法。
11.Testing Error Distribution by Kernelized Stein Discrepancy in Multivariate Time Series Models
多元时间序列模型中误差分布的核化Stein方差检验
Donghang Luo, Ke Zhu, Huan Gong & Dong Li
Knowing the error distribution is important in many multivariate time series applications. To alleviate the risk of error distribution mis-specification, testing methodologies are needed to detect whether the chosen error distribution is correct. However, the majority of existing tests only deal with the multivariate normal distribution for some special multivariate time series models, and thus cannot be used for testing the often observed heavy-tailed and skewed error distributions in applications. In this article, we construct a new consistent test for general multivariate time series models, based on the kernelized Stein discrepancy. To account for the estimation uncertainty and unobserved initial values, a bootstrap method is provided to calculate the critical values. Our new test is easy-to-implement for a large scope of multivariate error distributions, and its importance is illustrated by simulated and real data. As an extension, we also show how to test for the error distribution in copula time series models.
在许多多元时间序列的应用中，了解误差分布是很重要的。为了减轻错误分布不规范的风险，需要测试方法来检测所选择的错误分布是否正确。然而，现有的检验方法大多只处理一些特殊的多元时间序列模型的多元正态分布，无法对应用中经常观测到的重尾和偏态误差分布进行检验。本文基于核化斯坦差异构造了一种适用于一般多元时间序列模型的一致性检验。为了考虑到估计的不确定性和不可观测的初始值，提供了一种bootstrap方法来计算临界值。我们的新测试方法易于在大范围的多元误差分布中实现，并通过模拟和真实数据说明了它的重要性。作为一个扩展，我们还展示了如何在copula时间序列模型中测试误差分布。
12.Inference in Sparsity-Induced Weak Factor Models
稀疏性诱导的弱因子模型中的推理
Yoshimasa Uematsu & Takashi Yamagata
In this article, we consider statistical inference for high-dimensional approximate factor models. We posit a weak factor structure, in which the factor loading matrix can be sparse and the signal eigenvalues may diverge more slowly than the cross-sectional dimension, N. We propose a novel inferential procedure to decide whether each component of the factor loadings is zero or not, and prove that this controls the false discovery rate (FDR) below a preassigned level, while the power tends to unity. This “factor selection” procedure is primarily based on a debiased version of the sparse orthogonal factor regression (SOFAR) estimator; but is also applicable to the principal component (PC) estimator. After the factor selection, the resparsified SOFAR and sparsified PC estimators are proposed and their consistency is established. Finite sample evidence supports the theoretical results. We apply our method to the FRED-MD dataset of macroeconomic variables and the monthly firm-level excess returns which constitute the S&P 500 index. The results give very strong statistical evidence of sparse factor loadings under the identification restrictions and exhibit clear associations of factors and categories of the variables. Furthermore, our method uncovers a very weak but statistically significant factor in the residuals of Fama-French five factor regression.
在本文中，我们考虑高维近似因子模型的统计推断。我们假设了一个弱因子结构，其中因子载荷矩阵可以是稀疏的，信号特征值可能比横截面维n发散得更慢。我们提出了一个新的推理程序来决定因子载荷的每个组成部分是否为零，并证明了这将控制错误发现率(FDR)低于预先指定的水平，而功率趋于统一。这个“因子选择”过程主要基于稀疏正交因子回归(SOFAR)估计的去偏版本;但也适用于主成分(PC)估计。在因子选择之后，提出了重分类SOFAR和稀疏PC估计量，并建立了它们的一致性。有限样本证据支持理论结果。我们将我们的方法应用于FRED-MD宏观经济变量数据集和构成标准普尔500指数的公司层面月度超额收益。结果给出了非常强大的统计证据，稀疏的因素载荷下识别限制，并显示出明确的关联因素和类别的变量。此外，我们的方法在Fama-French五因子回归的残差中发现了一个非常弱但在统计上显著的因素。
13.Optimal Shrinkage-Based Portfolio Selection in High Dimensions
高维下基于收缩的最优投资组合选择
Taras Bodnar, Yarema Okhrin & Nestor Parolya
In this article, we estimate the mean-variance portfolio in the high-dimensional case using the recent results from the theory of random matrices. We construct a linear shrinkage estimator which is distribution-free and is optimal in the sense of maximizing with probability 1 the asymptotic out-of-sample expected utility, that is, mean-variance objective function for different values of risk aversion coefficient which in particular leads to the maximization of the out-of-sample expected utility and to the minimization of the out-of-sample variance. One of the main features of our estimator is the inclusion of the estimation risk related to the sample mean vector into the high-dimensional portfolio optimization. The asymptotic properties of the new estimator are investigated when the number of assets p and the sample size n tend simultaneously to infinity such that p/n→c∈(0,+∞). The results are obtained under weak assumptions imposed on the distribution of the asset returns, namely the existence of the 4+ε moments is only required. Thereafter we perform numerical and empirical studies where the small- and large-sample behavior of the derived estimator is investigated. The suggested estimator shows significant improvements over the existent approaches including the nonlinear shrinkage estimator and the three-fund portfolio rule, especially when the portfolio dimension is larger than the sample size. Moreover, it is robust to deviations from normality.
在本文中，我们利用随机矩阵理论的最新结果来估计高维情况下的平均方差投资组合。我们构造了一个线性收缩估计，它是无分布的，并且在以概率1最大化渐近样本外期望效用的意义上是最优的，即不同风险规避系数值的均值-方差目标函数，它特别导致了样本外期望效用的最大化和样本外方差的最小化。我们的估计器的一个主要特征是将与样本均值向量相关的估计风险纳入高维投资组合优化中。研究了当资产数量p和样本容量n同时趋于无穷，使得p/n→c∈(0，+∞)时，新估计量的渐近性质。这一结果是在对资产收益率分布的弱假设下得到的，即只需要存在4+ε矩。随后，我们进行了数值和实证研究，其中研究了导出的估计量的小样本和大样本行为。与已有的非线性收缩估计法和三基金组合规则估计法相比，所提出的估计方法有了明显的改进，特别是当投资组合维数大于样本量时。此外，该模型对偏离常态的情况具有较强的稳健性。
14.Kernel Averaging Estimators
核平均估计量
Rong Zhu, Xinyu Zhang, Alan T. K. Wan & Guohua Zou
The issue of bandwidth selection is a fundamental model selection problem stemming from the uncertainty about the smoothness of the regression. In this article, we advocate a model averaging approach to circumvent the problem caused by this uncertainty. Our new approach involves averaging across a series of Nadaraya-Watson kernel estimators each under a different bandwidth, with weights for these different estimators chosen such that a least-squares cross-validation criterion is minimized. We prove that the resultant combined-kernel estimator achieves the smallest possible asymptotic aggregate squared error. The superiority of the new estimator over estimators based on widely accepted conventional bandwidth choices in finite samples is demonstrated in a simulation study and a real data example.
带宽选择问题是一个基本的模型选择问题，它源于回归的平滑性的不确定性。在本文中，我们提出了一种模型平均方法来规避这种不确定性带来的问题。我们的新方法涉及在不同带宽下对一系列Nadaraya-Watson核估计进行平均，为这些不同的估计选择权重，以便最小化最小二乘交叉验证准则。我们证明了所得到的组合核估计达到了最小的可能渐近聚集平方误差。在一个仿真研究和一个真实的数据例子中，我们证明了新的估计器相对于在有限样本中广泛接受的传统带宽选择的估计器的优越性。
15.Time Series Approach to the Evolution of Networks: Prediction and Estimation
网络演化的时间序列方法:预测和估计
Anna Bykhovskaya
The article analyzes nonnegative multivariate time series which we interpret as weighted networks. We introduce a model where each coordinate of the time series represents a given edge across time. The number of time periods is treated as large compared to the size of the network. The model specifies the temporal evolution of a weighted network that combines classical autoregression with nonnegativity, a positive probability of vanishing, and peer effect interactions between weights assigned to edges in the process. The main results provide criteria for stationarity versus explosiveness of the network evolution process and techniques for estimation of the parameters of the model and for prediction of its future values. Natural applications arise in networks of fixed number of agents, such as countries, large corporations, or small social communities. The article provides an empirical implementation of the approach to monthly trade data in European Union. Overall, the results confirm that incorporating nonnegativity of dependent variables into the model matters and incorporating peer effects leads to the improved prediction power.
本文分析了非负的多元时间序列，并将其解释为加权网络。我们引入了一个模型，其中时间序列的每个坐标表示跨时间的给定边。与网络的规模相比，时间周期的数量被视为较大的。该模型指定了一个加权网络的时间演变，该网络结合了经典自回归与非负性、消失的正概率以及在此过程中分配给边缘的权值之间的同伴效应相互作用。研究结果为网络演化过程的平稳性和爆发性提供了标准，为模型参数的估计和未来数值的预测提供了技术依据。自然应用出现在固定数量的代理网络中，如国家、大公司或小型社会社区。本文对欧盟的月度贸易数据进行了实证分析。总体而言，结果证实了将因变量的非负性纳入模型具有重要意义，并将同群效应纳入模型能够提高预测能力。
16.Test for Market Timing Using Daily Fund Returns
利用每日基金收益测试市场择时
Lei Jiang, Weimin Liu & Liang Peng
Using daily mutual fund returns to estimate market timing, some econometric issues, including heteroscedasticity, correlated errors, and heavy tails, make the traditional least-squares estimate in Treynor–Mazuy and Henriksson–Merton models biased and severely distort the t-test size. Using ARMA-GARCH models, weighted least-squares estimate to ensure a normal limit, and random weighted bootstrap method to quantify uncertainty, we find more funds with positive timing ability than the Newey–West t-test. Empirical evidence indicates that funds with perverse timing ability have high fund turnovers and funds tradeoff between timing and stock picking skills.
摘要利用共同基金日收益来估计市场时机，由于存在异方差、相关误差和重尾等计量经济学问题，使得传统的Treynor-Mazuy模型和Henriksson-Merton模型的最小二乘估计存在偏差，严重扭曲了t检验的大小。本文利用ARMA-GARCH模型、加权最小二乘估计和随机加权bootstrap方法来量化基金的不确定性，结果表明，相对于Newey-West t检验，基金更具有正择时能力。实证结果表明，择时能力较差的基金具有较高的基金周转率和择时与选股能力之间的权衡。
17.Survey Response Behavior as a Proxy for Unobserved Ability: Theory and Evidence
作为不可观察能力的代表的调查反应行为:理论和证据
Sonja C. de New & Stefanie Schurer
An emerging literature is experimenting with using survey response behavior as a proxy for hard-to-measure abilities. We contribute to this literature by formalizing this idea and evaluating its benefits and risks. Using a standard and nationally representative survey from Australia, we demonstrate that the survey item-response rate (SIRR), a straightforward summary measure of response behavior, varies more with cognitive than with noncognitive ability. We evaluate whether SIRR is a useful proxy to reduce ability-related biases in a standard economic application. We show empirically that SIRR, although a weak and imperfect proxy, leads to omitted-variable bias reductions of up to 20%, and performs better than other proxy variables derived from paradata. Deriving the necessary and sufficient conditions for a valid proxy, we show that a strong proxy is neither a necessary nor a sufficient condition to reduce estimation biases. A critical consideration is to which degree the proxy introduces a multicollinearity problem, a finding of general interest. We illustrate the theoretical derivations with an empirical application.
一种新兴的文献正在尝试使用调查反应行为作为难以衡量的能力的代理。我们通过将这一想法正式化并评估其收益和风险来贡献这一文献。使用来自澳大利亚的一项标准的、具有全国代表性的调查，我们证明了调查项目反应率(SIRR)，一种直接的反应行为的总结测量，在认知能力方面的差异大于非认知能力。我们评估SIRR是否在标准经济应用中是减少能力相关偏差的有用代理。我们的经验表明，SIRR虽然是一个弱的和不完美的代理变量，但导致遗漏变量偏差降低高达20%，并优于其他从paradata派生的代理变量。通过推导有效代理的充要条件，我们证明了强代理既不是减少估计偏差的必要条件，也不是充分条件。一个关键的考虑是，代理在多大程度上引入了多重共线性问题，一个普遍感兴趣的发现。我们举例说明了理论推导与经验应用。
18.Estimation of Sparsity-Induced Weak Factor Models
稀疏性诱发的弱因子模型的估计
Yoshimasa Uematsu & Takashi Yamagata
This article investigates estimation of sparsity-induced weak factor (sWF) models, with large cross-sectional and time-series dimensions (N and T, respectively). It assumes that the kth largest eigenvalue of a data covariance matrix grows proportionally to Nαk with unknown exponents 0<αk≤1 for k=1,…,r. Employing the same rotation of the principal components (PC) estimator, the growth rate αk is linked to the degree of sparsity of kth factor loadings. This is much weaker than the typical assumption on the recent factor models, in which all the r largest eigenvalues diverge proportionally to N. We apply the method of sparse orthogonal factor regression (SOFAR) by Uematsu et al. (2019) to estimate the sWF models and derive the estimation error bound. Importantly, our method also yields consistent estimation of αk. A finite sample experiment shows that the performance of the new estimator uniformly dominates that of the PC estimator. We apply our method to forecasting bond yields and the results demonstrate that our method outperforms that based on the PC. We also analyze S&P500 firm security returns and find that the first factor is consistently near strong while the others are weak.
本文研究了具有大截面和时间序列维数(分别为N和T)的稀疏诱导弱因子(sWF)模型的估计。假设数据协方差矩阵的第k个最大特征值与Nαk成比例增长，未知指数为0<当k=1，…，r时，αk≤1。利用主成分(PC)估计器的相同旋转，增长率αk与第k个因子载荷的稀疏程度相联系。这比最近的因子模型中所有r最大特征值与n成比例发散的典型假设要弱得多。重要的是，我们的方法也得到了αk的一致估计。有限样本实验表明，新估计器的性能一致优于原估计器。我们将我们的方法应用于债券收益率的预测，结果表明，我们的方法优于基于PC的方法。我们还分析了标准普尔500指数(S&P500)公司的证券回报，发现第一个因素一直接近强势，而其他因素则较弱。
19.Testing for Structural Change of Predictive Regression Model to Threshold Predictive Regression Model
预测回归模型向阈值预测回归模型的结构变化检验
Fukang Zhu, Mengya Liu, Shiqing Ling & Zongwu Cai
This article investigates two test statistics for testing structural changes and thresholds in predictive regression models. The generalized likelihood ratio (GLR) test is proposed for the stationary predictor and the generalized F test is suggested for the persistent predictor. Under the null hypothesis of no structural change and threshold, it is shown that the GLR test statistic converges to a function of a centered Gaussian process, and the generalized F test statistic converges to a function of Brownian motions. A Bootstrap method is proposed to obtain the critical values of test statistics. Simulation studies and a real example are given to assess the performances of the proposed tests.
本文研究了预测回归模型中检验结构变化和阈值的两个检验统计量。对平稳预测量采用广义似然比(GLR)检验，对持续预测量采用广义F检验。在无结构变化和阈值的零假设下，GLR检验统计量收敛于中心高斯过程的函数，广义F检验统计量收敛于布朗运动的函数。提出了一种Bootstrap方法来获取检验统计量的临界值。通过仿真研究和实例验证了所提出的测试方法的性能。
20.Bootstrap Tests for High-Dimensional White-Noise
高维白噪声的Bootstrap检验
Lengyang Wang, Efang Kong & Yingcun Xia
The testing of white-noise (WN) is an essential step in time series analysis. In a high dimensional set-up, most existing methods either are computationally infeasible, or suffer from highly distorted Type-I errors, or both. We propose an easy-to-implement bootstrap method for high-dimensional WN test and prove its consistency for a variety of test statistics. Its power properties as well as extensions to WN tests based on fitted residuals are also considered. Simulation results show that compared to the existing methods, the new approach possesses much better power, while maintaining a proper control over the Type-I error. They also provide proofs that even in cases where our method is expected to suffer from lack of theoretical justification, it continues to outperform its competitors. The proposed method is applied to the analysis of the daily stock returns of the top 50 companies by market capitalization listed on the NYSE, and we find strong evidence that the common market factor is the main cause of cross-correlation between stocks.
白噪声检验是时间序列分析中必不可少的步骤。在高维设置中，大多数现有的方法要么在计算上不可行的，要么遭受高度扭曲的第一类错误，或者两者兼有。提出了一种易于实现的高维WN检验bootstrap方法，并证明了该方法对多种检验统计量的一致性。它的功率特性以及基于拟合残差的WN测试的扩展也被考虑。仿真结果表明，与现有方法相比，新方法在保持对第一类错误的适当控制的同时，具有更好的功率。他们还提供了证据，证明即使在我们的方法预计会缺乏理论理由的情况下，它仍然优于其竞争对手。将本文提出的方法应用于纽约证券交易所上市公司按市值计算的前50家公司的每日股票收益率分析，我们发现，共同市场因素是股票之间相互关联的主要原因。
21.Extreme Value Estimation for Heterogeneous Data
异质性数据的极值估计
John H. J. Einmahl & Yi He
We develop a universal econometric formulation of empirical power laws possibly driven by parameter heterogeneity. Our approach extends classical extreme value theory to specifying the tail behavior of the empirical distribution of a general dataset with possibly heterogeneous marginal distributions. We discuss several model examples that satisfy our conditions and demonstrate in simulations how heterogeneity may generate empirical power laws. We observe a cross-sectional power law for the U.S. stock losses and show that this tail behavior is largely driven by the heterogeneous volatilities of the individual assets.
我们开发了一个普遍的计量经济学公式的经验权力法律可能驱动的参数异质性。我们的方法扩展了经典的极值理论，以指定尾部行为的经验分布的一般数据集可能具有异质性的边际分布。我们讨论了几个满足我们条件的模型例子，并在模拟中演示了异质性如何产生经验幂律。我们观察到美国股票损失的横截面幂律，并表明这种尾部行为在很大程度上是由单个资产的异质性波动驱动的。
22.Factor and Factor Loading Augmented Estimators for Panel Regression With Possibly Nonstrong Factors
可能存在非强因素的面板回归的因子和因子负荷增强估计
Jad Beyhum & Eric Gautier
This article considers linear panel data models where the dependence of the regressors and the unobservables is modeled through a factor structure. The number of time periods and the sample size both go to infinity. Unlike in most existing methods for the estimation of this type of models, nonstrong factors are allowed and the number of factors can grow to infinity with the sample size. We study a class of two-step estimators of the regression coefficients. In the first step, factors and factor loadings are estimated. Then, the second step corresponds to the panel regression of the outcome on the regressors and the estimates of the factors and the factor loadings from the first step. The estimators enjoy double robustness. Different methods can be used in the first step while the second step is unique. We derive sufficient conditions on the first-step estimator and the data generating process under which the two-step estimator is asymptotically normal. Assumptions under which using an approach based on principal components analysis in the first step yields an asymptotically normal estimator are also given. The two-step procedure exhibits good finite sample properties in simulations. The approach is illustrated by an empirical application on fiscal policy.
本文考虑线性面板数据模型，其中回归变量和不可观察变量的依赖关系通过因子结构建模。时间周期的数量和样本容量都趋于无穷大。与大多数现有的模型估计方法不同，非强因子是允许的，并且因子的数量可以随着样本量的增加而无限增长。研究了回归系数的一类两步估计。第一步，估计因子和因子载荷。然后，第二步对应第一步对回归变量的结果和因子估计及因子载荷进行面板回归。估计量具有双重稳健性。第一步可以使用不同的方法，而第二步是唯一的。给出了第一步估计量渐近正态的充分条件和两步估计量渐近正态的数据生成过程。给出了第一步基于主成分分析的方法得到渐近正态估计的假设条件。两步方法在模拟中显示出良好的有限样本特性。财政政策的实证应用说明了这种方法。
